there is no multicollinearity in the data. The independent variables are linearly independent of each other, i.e.The independent variables are measured without error.Independent error assumption: It is assumed that the residual terms are independent of each other, i.e.Constant variance assumption: It is assumed that the residual terms have the same (but unknown) variance, σ 2 This assumption is also known as the assumption of homogeneity or homoscedasticity.Zero mean assumption: It is assumed that the residuals have a mean value of zero.Normality assumption: It is assumed that the error terms, ε (i), are normally distributed.It is known as the ‘linearity assumption’. It is assumed that there is a linear relationship between the dependent and independent variables. The assumption about the form of the model:.There are three main assumptions in a linear regression model: State the assumptions in a linear regression model. It is mostly done by the Sum of Squared Residuals Method. In technical terms, linear regression is a machine learning algorithm that finds the best linear-fit relationship on any given data, between independent and dependent variables. finding the best linear relationship between the independent and dependent variables. In simple terms, linear regression is a method of finding the best straight line fitting to the given data, i.e. Let’s get started with linear regression! 1. Decision Trees and Questions which pertain to all algorithms.Q&As on these algorithms will be provided in a series of four blog posts.Įach blog post will cover the following topic:. Linear Regression interview questions are the most common in Machine Learning interviews. Based on their extensive feedback a set of question and answers were prepared to help aspiring data scientists in their conversations. We consulted hiring managers and data scientists from various organisations to know about the typical ML questions which they ask in an interview. Data scientists are expected to possess an in-depth knowledge of these algorithms. These conventional algorithms being linear regression, logistic regression, clustering, decision trees etc. It is a common practice to test data science aspirants on commonly used machine learning algorithms in interviews. In this article, we’ll examine the most typical machine learning interview questions and offer helpful preparation advice for each of them. Different facets of your expertise and knowledge in the topic are assessed in each category. The categories included in these interviews are frequently coding, machine learning concepts, screening, and system design. Getting your desired job as a machine learning engineer may need you to pass a machine learning interview. We will focus on the most common types of categories and how to prepare for them. When going for the role of Machine Learning Engineer interview, they can specialize in categories like Coding, Research, Case Study, Project Management, Presentation, System Design, and Statistics. Explain how you have used cloud computing platforms like AWS or Azure to build scalable and cost-effective data infrastructure, and highlight any experience you have with infrastructure as code tools like Terraform or CloudFormation.Machine Learning Interviews can vary according to the types or categories, for instance, a few recruiters ask many Linear Regression interview questions. Explain how you have optimized data pipelines for performance and reliability, and highlight any experience you have with stream processing technologies like Apache Kafka.ĭata Infrastructure: To answer questions about data infrastructure, emphasize your experience designing and managing data infrastructure, including databases, data lakes, and data warehouses. Discuss your experience with data integration, data quality, and data governance in the context of data warehousing, and explain how you have used ETL tools like Informatica or Talend to move data into and out of data warehouses.ĭata Pipelines: When answering questions about data pipelines, focus on your experience building and managing data pipelines using tools like Apache NiFi or Apache Airflow. Explain how you have used these techniques to design effective data models, and highlight any experience you have with data modeling tools like ERwin or Visio.ĭata Warehousing: To answer questions about data warehousing, emphasize your experience working with data warehouses and how you have optimized them for performance. Data Modeling: When answering IT interview questions about data modeling, focus on your experience working with various data modeling techniques, such as entity-relationship diagrams or dimensional modeling.
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